• Title/Summary/Keyword: Matching prior

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Noninformative Priors for Fieller-Creasy Problem using Unbalanced Data

  • Kim, Dal-Ho;Lee, Woo-Dong;Kang, Sang-Gil
    • 한국데이터정보과학회:학술대회논문집
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    • 2005.10a
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    • pp.71-84
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    • 2005
  • The Fieller-Creasy problem involves statistical inference about the ratio of two independent normal means. It is difficult problem from either a frequentist or a likelihood perspective. As an alternatives, a Bayesian analysis with noninformative priors may provide a solution to this problem. In this paper, we extend the results of Yin and Ghosh (2001) to unbalanced sample case. We find various noninformative priors such as first and second order matching priors, reference and Jeffreys' priors. The posterior propriety under the proposed noninformative priors will be given. Using real data, we provide illustrative examples. Through simulation study, we compute the frequentist coverage probabilities for probability matching and reference priors. Some simulation results will be given.

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Noninformative priors for linear function of parameters in the lognormal distribution

  • Lee, Woo Dong;Kim, Dal Ho;Kang, Sang Gil
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.4
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    • pp.1091-1100
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    • 2016
  • This paper considers the noninformative priors for the linear function of parameters in the lognormal distribution. The lognormal distribution is applied in the various areas, such as occupational health research, environmental science, monetary units, etc. The linear function of parameters in the lognormal distribution includes the expectation, median and mode of the lognormal distribution. Thus we derive the probability matching priors and the reference priors for the linear function of parameters. Then we reveal that the derived reference priors do not satisfy a first order matching criterion. Under the general priors including the derived noninformative priors, we check the proper condition of the posterior distribution. Some numerical study under the developed priors is performed and real examples are illustrated.

Noninformative Priors in Freund's Bivariate Exponential Distribution : Symmetry Case

  • Cho, Jang-Sik;Baek, Sung-Uk;Kim, Hee-Jae
    • Journal of the Korean Data and Information Science Society
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    • v.13 no.2
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    • pp.235-242
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    • 2002
  • In this paper, we develop noninformative priors that are used for estimating the ratio of failure rates under Freund's bivariate exponential distribution. A class of priors is found by matching the coverage probabilities of one-sided Baysian credible interval with the corresponding frequentist coverage probabilities. Also the propriety of posterior under the noninformative priors is proved and the frequentist coverage probabilities are investigated for small samples via simulation study.

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Bayesian Hypothesis Testing for the Difference of Quantiles in Exponential Models

  • Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.4
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    • pp.1379-1390
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    • 2008
  • This article deals with the problem of testing the difference of quantiles in exponential distributions. We propose Bayesian hypothesis testing procedures for the difference of two quantiles under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the matching prior. Simulation study and a real data example are provided.

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Noninformative Priors for the Intraclass Coefficient of a Symmetric Normal Distribution

  • Chang, In-Hong;Kim, Byung-Hwee
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.10a
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    • pp.15-19
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    • 2003
  • In this paper, we develop the Jeffreys' prior, reference priors and the probability matching priors for the intraclass correlation coefficient of a symmetric normal distribution. We next verify propriety of posterior distributions under those noninformative priors. We examine whether reference priors satisfy the probability matching criterion.

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Bayesian Test for the Difference of Exponential Guarantee Time Parameters

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • 한국데이터정보과학회:학술대회논문집
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    • 2004.04a
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    • pp.15-23
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    • 2004
  • When X and Y have independent two parameter exponential distributions, we develop a Bayesian testing procedures for the equality of two location parameters. Under the noninformative prior, we propose a Bayesian test procedures for the equality of two location parameters using fractional Bayes factor and intrinsic Bayes factor. Simulation study and some real data examples are provided.

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Rectifying Inspection Plan for KS A 3102 with Gamma-Prior Distribution (Gamma-Prior가 고려된 KS A 3102의 수정검사방식(修正檢査方式))

  • Jeong, Yeong-Bae;Hwang, Ui-Cheol
    • Journal of Korean Society for Quality Management
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    • v.15 no.2
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    • pp.55-60
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    • 1987
  • A rectifying inspection plan which assumes a gamma - prior distribution on the lot percent defective is considered. This sampling inspection plan is developed for finite lot sizes with matching OC curves and generated from an initial plan selected from KS A 3102 single sampling by attributes. Comparisons are made with each plan by three examples.

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Incorporation of Scene Geometry in Least Squares Correlation Matching for DEM Generation from Linear Pushbroom Images

  • Kim, Tae-Jung;Yoon, Tae-Hun;Lee, Heung-Kyu
    • Proceedings of the KSRS Conference
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    • 1999.11a
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    • pp.182-187
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    • 1999
  • Stereo matching is one of the most crucial parts in DEM generation. Naive stereo matching algorithms often create many holes and blunders in a DEM and therefore a carefully designed strategy must be employed to guide stereo matching algorithms to produce “good” 3D information. In this paper, we describe one such a strategy designed by the use of scene geometry, in particular, the epipolarity for generation of a DEM from linear pushbroom images. The epipolarity for perspective images is a well-known property, i.e., in a stereo image pair, a point in the reference image will map to a line in the search image uniquely defined by sensor models of the image pair. This concept has been utilized in stereo matching by applying epipolar resampling prior to matching. However, the epipolar matching for linear pushbroom images is rather complicated. It was found that the epipolarity can only be described by a Hyperbola- shaped curve and that epipolar resampling cannot be applied to linear pushbroom images. Instead, we have developed an algorithm of incorporating such epipolarity directly in least squares correlation matching. Experiments showed that this approach could improve the quality of a DEM.

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The Movement Order of the νP-Subject and the VP-Object in English

  • Lee, Doo-Won
    • Korean Journal of English Language and Linguistics
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    • v.4 no.1
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    • pp.103-116
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    • 2004
  • Chomsky (2001) and Kitahara's (2002) suggestion that object shift occurs prior to movement of the νP-subject to SPEC-T is not on the right track with respect to the Merge operation. According to the Merge operation, TP is necessarily created earlier than CP. Chomsky (2001) suggests that the probe-goal relation between T and SUBJ is evaluated in the CP after it is known whether the position of as has become a trace losing its phonological content. However, the FocP is not a phase (CP). So, Chomsky (2001) and Kitahara's (2002) suggestion is not correct in the case of the movement of OBJ to the spec of Foc in English, either. The aim of this paper is to show that the νP-subject must move to SPEC- T prior to the consecutive movement of the wh-object to SPEC-C via object shift in English. This derivation obeys Chomsky's (2001) so-called probe-goal matching condition.

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BAYESIAN INFERENCE FOR FIELLER-CREASY PROBLEM USING UNBALANCED DATA

  • Lee, Woo-Dong;Kim, Dal-Ho;Kang, Sang-Gil
    • Journal of the Korean Statistical Society
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    • v.36 no.4
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    • pp.489-500
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    • 2007
  • In this paper, we consider Bayesian approach to the Fieller-Creasy problem using noninformative priors. Specifically we extend the results of Yin and Ghosh (2000) to the unbalanced case. We develop some noninformative priors such as the first and second order matching priors and reference priors. Also we prove the posterior propriety under the derived noninformative priors. We compare these priors in light of how accurately the coverage probabilities of Bayesian credible intervals match the corresponding frequentist coverage probabilities.